Libraries:

# libraries -------------------
library(dplyr)
library(tidyverse)
library(ggplot2)
library(GGally)
library(glmmTMB)
library(TMB)
library(emmeans)
library(DHARMa)
library(lmtest)

Set working directory

knitr::opts_knit$set(root.dir = '/Users/user/Desktop/Data/Regen_RProj/')

Functions:

# functions -------------------

Source large seedling data:

source("Scripts/LS_Import.R")

Pivot wider to create dataframe where each row is for one plot and has total details for each species group

ls_merge2 <- ls_merge %>% 
  select(Plot_No, Region, Treat_Type, Site, Species_Groups, Total) #this is dropping browse and stump sprout data

ls_merge2 <- ls_merge2 %>% 
  pivot_wider(names_from = Species_Groups, values_from = Total)

Import time since data and add it to the large seedling dataset

time_since <- read_csv("CleanData/Treat_Year_Data.csv")

ls_merge3 <- merge(ls_merge2, time_since, by = 'Site')
#log transform time from treatment data
ls_merge3$l.TFT <- log(ls_merge3$Time_from_Treat)

Run the ‘Add_BA’ script and merge with dataset:

source("Scripts/Add_BA.R")

# merge with ls dataset -------------------
ls_merge4 <- merge(ls_merge3, prism_BA, by = "Plot_No")

Run ‘Ground_Data.R’ script and add it to large seedling dataset:

source("Scripts/Ground_Data.R")

# merge with ls dataset -------------------
ls_merge5 <- merge(ls_merge4, ground3, by = "Plot_No")

Source and add veg data

source("Scripts/Veg_Data.R")

# merge with ls dataset 
ls.all <- merge(ls_merge5, veg3, by = "Plot_No")

rm(ls_merge5,
   ls_merge2,
   ls_merge3,
   ls_merge4)

The large seedling count data is taken in 10m² plots; basal area is measured in hectares; veg and soil data is taken in 1m² plots.

Large seedling data will be converted into 1m² plots in order to compare across and reduce the amount of scales of data collection to two: 1m² plots and per hectare observations.

ls.all$PIRI.1m <- ls.all$PIRI/10
ls.all$SO.1m <- ls.all$Shrub_Oak/10
ls.all$Other.1m <- ls.all$Other/10

Create log transformed categories for newly added variables, then select for just the desired variables:

ls.all$l.PIRI1 <- log(ls.all$PIRI.1m + 1)
ls.all$l.SO1 <- log(ls.all$SO.1m + 1)
ls.all$l.other1 <- log(ls.all$Other.1m + 1)

ls.all2 <- ls.all %>% 
  select(Treat_Type, Region, Site, Plot_No, PIRI, PIRI.1m, l.PIRI1, Shrub_Oak, SO.1m, l.SO1, Other, Other.1m, l.other1, Time_from_Treat, l.TFT, BA_HA, l.BA_HA, PIRI.BA_HA, l.BA_piri, Mineral_Soil, l.Mineral, Litter_Duff, avgLD, avgLD_l, Veg_Total, l.Veg_Total) %>% 
  arrange(Treat_Type)

Select just for numerical vs log and then look at paired plots:

#not transformed
ls.num <- ls.all2 %>% 
  select(PIRI, Shrub_Oak, Other, Time_from_Treat, BA_HA, PIRI.BA_HA, Mineral_Soil, avgLD, Veg_Total, Treat_Type)

ggpairs(ls.num)
ggpairs(ls.num, aes(color = Treat_Type))


#log transformed
ls.numl <- ls.all2 %>% 
  select(l.PIRI, l.SO, l.other, l.TFT, l.BA_HA, l.BA_piri, l.Mineral, avgLD_l, l.Veg_Total, Treat_Type)

ggpairs(ls.numl)
ggpairs(ls.numl, aes(color = Treat_Type))

rm(ls.num,
   ls.numl)

Can see the correlation coefficients for linear (Pearsons) relationships. None of them appear very strong, except for ones that are analogs (avg LD vs mineral soil exposure; ba/ha vs piri ba/ha)

Log transformed average litter depth and basal area per hectare have a weak relationship (corr 0.33), which does make sense.

The above script a data naming conventions are the same as in the LS_GLMM script

Full dataset is called ls.all2

Starting first with model analysis without treatment type:

ls.m1 <- glmmTMB(PIRI.1m ~ l.SO1 + l.other1 + l.BA_HA + l.BA_piri + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all2,
                 family = poisson)

## Warning in glmmTMB(PIRI.1m ~ l.SO1 + l.other1 + l.BA_HA + l.BA_piri + l.Mineral
## + : non-integer counts in a poisson model

AIC(ls.m1) #98.5

## [1] 98.45309

# gives warning about non-integer values (as i've reduced the counts (/10) to get them to the 1m2 ) - if BA isn't important, I could try again with no BA in dataset and keep 10m2 observations ....
summary(ls.m1)

##  Family: poisson  ( log )
## Formula:          
## PIRI.1m ~ l.SO1 + l.other1 + l.BA_HA + l.BA_piri + l.Mineral +  
##     avgLD_l + l.Veg_Total + offset(l.TFT) + (1 | Site/Plot_No)
## Data: ls.all2
## 
##      AIC      BIC   logLik deviance df.resid 
##     98.5    140.6    -39.2     78.5      488 
## 
## Random effects:
## 
## Conditional model:
##  Groups       Name        Variance  Std.Dev. 
##  Plot_No:Site (Intercept) 2.561e-09 5.061e-05
##  Site         (Intercept) 6.410e+00 2.532e+00
## Number of obs: 498, groups:  Plot_No:Site, 498; Site, 47
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.69499    3.33513  -1.408    0.159
## l.SO1       -1.13705    1.00373  -1.133    0.257
## l.other1    -0.21927    1.53411  -0.143    0.886
## l.BA_HA     -0.28410    0.59909  -0.474    0.635
## l.BA_piri   -0.38588    0.60304  -0.640    0.522
## l.Mineral    0.08863    0.34845   0.254    0.799
## avgLD_l     -0.08137    0.87393  -0.093    0.926
## l.Veg_Total -0.33215    0.65895  -0.504    0.614

ls.m2 <- glmmTMB(PIRI.1m ~ l.SO1 + l.other1 + l.BA_HA + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all2,
                 family = poisson)

## Warning in glmmTMB(PIRI.1m ~ l.SO1 + l.other1 + l.BA_HA + l.Mineral + avgLD_l +
## : non-integer counts in a poisson model

AIC(ls.m2) #96.8

## [1] 96.83059

lrtest(ls.m1, ls.m2) #p = 0.5

## Likelihood ratio test
## 
## Model 1: PIRI.1m ~ l.SO1 + l.other1 + l.BA_HA + l.BA_piri + l.Mineral + 
##     avgLD_l + l.Veg_Total + offset(l.TFT) + (1 | Site/Plot_No)
## Model 2: PIRI.1m ~ l.SO1 + l.other1 + l.BA_HA + l.Mineral + avgLD_l + 
##     l.Veg_Total + offset(l.TFT) + (1 | Site/Plot_No)
##   #Df  LogLik Df  Chisq Pr(>Chisq)
## 1  10 -39.227                     
## 2   9 -39.415 -1 0.3775     0.5389

ls.m3 <- glmmTMB(PIRI.1m ~ l.SO1 + l.other1 + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all2,
                 family = poisson)

## Warning in glmmTMB(PIRI.1m ~ l.SO1 + l.other1 + l.Mineral + avgLD_l +
## l.Veg_Total + : non-integer counts in a poisson model

AIC(ls.m3) #96.0

## [1] 96.00496

lrtest(ls.m2, ls.m3) # p = 0.3

## Likelihood ratio test
## 
## Model 1: PIRI.1m ~ l.SO1 + l.other1 + l.BA_HA + l.Mineral + avgLD_l + 
##     l.Veg_Total + offset(l.TFT) + (1 | Site/Plot_No)
## Model 2: PIRI.1m ~ l.SO1 + l.other1 + l.Mineral + avgLD_l + l.Veg_Total + 
##     offset(l.TFT) + (1 | Site/Plot_No)
##   #Df  LogLik Df  Chisq Pr(>Chisq)
## 1   9 -39.415                     
## 2   8 -40.002 -1 1.1744     0.2785

rm(ls.m1, ls.m2, ls.m3)

ls.m11a <- glmmTMB(PIRI.1m ~ avgLD_l + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all2,
                 family = poisson)

## Warning in glmmTMB(PIRI.1m ~ avgLD_l + offset(l.TFT) + (1 | Site/Plot_No), :
## non-integer counts in a poisson model

AIC(ls.m11a) #AIC = 89.3, was just interested to see the AIC of this model vs. ls at 10m2 observations

## [1] 89.37025

#i'm just interested to see model fit on this 1m model
ls.m11a_sr <-  simulateResiduals(ls.m11a, n = 1000, plot = T) #fails very much so

Basal area doesn’t seem important, so I’m going to go back to the 10m² and 1m² scales and lose per hectare observations. I’ll need to rework the dataset

Revised data set with LS observations at 10m² scale; this means no non-interger values for the poisson distro

ls.all3 <- ls.all

ls.all3$l.PIRI <- log(ls.all3$PIRI + 1)
ls.all3$l.SO <- log(ls.all3$Shrub_Oak + 1)
ls.all3$l.other <- log(ls.all3$Other + 1)

ls.all3 <- ls.all3 %>% 
  select(Treat_Type, Region, Site, Plot_No, PIRI, l.PIRI, Shrub_Oak, l.SO, Other, l.other, Time_from_Treat, l.TFT, BA_HA, l.BA_HA, PIRI.BA_HA, l.BA_piri, Mineral_Soil, l.Mineral, Litter_Duff, avgLD, avgLD_l, Veg_Total, l.Veg_Total) %>% 
  arrange(Treat_Type)

Begin modeling again:

#double checking about basal area variables, even though this is at 3 scales

ls.m4 <- glmmTMB(PIRI ~ l.SO + l.other + l.BA_HA + l.BA_piri + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m4) #AIC is 291


#test piri ba
ls.m5 <- glmmTMB(PIRI ~ l.SO + l.other + l.BA_HA + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m5) #289.2

lrtest(ls.m4, ls.m5) #p = 0.7

#test ba
ls.m6 <- glmmTMB(PIRI ~ l.SO + l.other + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m6) #287.3

lrtest(ls.m5, ls.m6) #p=0.8

#test mineral soil
ls.m7 <- glmmTMB(PIRI ~ l.SO + l.other + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m7) #285.3

lrtest(ls.m6, ls.m7) # p = 0.8

#test litter depth
ls.m8 <- glmmTMB(PIRI ~ l.SO + l.other + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m8) #289.5

lrtest(ls.m7, ls.m8) # p = 0.01, keep avg LD

#return avg ld, test other
ls.m9 <- glmmTMB(PIRI ~ l.SO + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m9) #285.1

lrtest(ls.m7, ls.m9) #p = 0.2

# test shrub oak
ls.m10 <- glmmTMB(PIRI ~ avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m10) #285.2

lrtest(ls.m10, ls.m9) #p = 0.1

#test veg cover ------ seems like this is the best model
ls.m11 <- glmmTMB(PIRI ~ avgLD_l + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m11) # p = 283.2


ls.m12 <- glmmTMB(PIRI ~ avgLD_l + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = genpois)
AIC(ls.m12) #280

ls.m12_sr <- simulateResiduals(ls.m12, n = 100, plot = TRUE)


lrtest(ls.m10, ls.m11) # p = 0.97

rm(ls.m4, ls.m5, ls.m6, ls.m7, ls.m8, ls.m9, ls.m10)

Now to test model fit:

ls.m11_sr <- simulateResiduals(ls.m11, n = 1000, plot = TRUE) #doesn't pass ks
testResiduals(ls.m11_sr)
testZeroInflation(ls.m11_sr)

# I wonder if I added treat type, if the model would pass
ls.m12 <- glmmTMB(PIRI ~ Treat_Type + avgLD_l + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m12) #284.6

lrtest(ls.m12, ls.m11) #says treat type isn't important

ls.m12_sr <- simulateResiduals(ls.m12, n = 1000, plot = T) #looks better, still not perfect

testResiduals(ls.m12_sr) #passes
testZeroInflation(ls.m12_sr) #passes
testQuantiles(ls.m12_sr)

#going to test a model with more variables (and not treat type), to see if that fits better


ls.m10_sr <- simulateResiduals(ls.m10, n = 1000, plot = T)
#tests on model 9 failed & on 10

Models without treatment type failed in model fit. Going to run variable elimination again, starting with models with treatment type

ls.m13 <- glmmTMB(PIRI ~ Treat_Type + l.SO + l.other + l.BA_HA + l.BA_piri + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m13) #293.2

## [1] 293.1687

ls.m14 <- glmmTMB(PIRI ~ Treat_Type + l.SO + l.other + l.BA_HA + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m14) #291.3

## [1] 291.3429

ls.m15 <- glmmTMB(PIRI ~ Treat_Type + l.SO + l.other + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m15) #289.5

## [1] 289.4573

ls.m16 <- glmmTMB(PIRI ~ Treat_Type + l.SO + l.other + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m16) #287.5

## [1] 287.4731

ls.m17 <- glmmTMB(PIRI ~ Treat_Type + l.SO + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m17) #286.3

## [1] 286.315

lrtest(ls.m13, ls.m14, ls.m15, ls.m16, ls.m17)

## Likelihood ratio test
## 
## Model 1: PIRI ~ Treat_Type + l.SO + l.other + l.BA_HA + l.BA_piri + l.Mineral + 
##     avgLD_l + l.Veg_Total + offset(l.TFT) + (1 | Site/Plot_No)
## Model 2: PIRI ~ Treat_Type + l.SO + l.other + l.BA_HA + l.Mineral + avgLD_l + 
##     l.Veg_Total + offset(l.TFT) + (1 | Site/Plot_No)
## Model 3: PIRI ~ Treat_Type + l.SO + l.other + l.Mineral + avgLD_l + l.Veg_Total + 
##     offset(l.TFT) + (1 | Site/Plot_No)
## Model 4: PIRI ~ Treat_Type + l.SO + l.other + avgLD_l + l.Veg_Total + 
##     offset(l.TFT) + (1 | Site/Plot_No)
## Model 5: PIRI ~ Treat_Type + l.SO + avgLD_l + l.Veg_Total + offset(l.TFT) + 
##     (1 | Site/Plot_No)
##   #Df  LogLik Df  Chisq Pr(>Chisq)
## 1  14 -132.58                     
## 2  13 -132.67 -1 0.1742     0.6764
## 3  12 -132.73 -1 0.1144     0.7352
## 4  11 -132.74 -1 0.0159     0.8998
## 5  10 -133.16 -1 0.8418     0.3589

ls.m18 <- glmmTMB(PIRI ~ Treat_Type + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m18) #286.6

## [1] 286.5651

lrtest(ls.m17, ls.m18) #p = 0.13

## Likelihood ratio test
## 
## Model 1: PIRI ~ Treat_Type + l.SO + avgLD_l + l.Veg_Total + offset(l.TFT) + 
##     (1 | Site/Plot_No)
## Model 2: PIRI ~ Treat_Type + avgLD_l + l.Veg_Total + offset(l.TFT) + (1 | 
##     Site/Plot_No)
##   #Df  LogLik Df  Chisq Pr(>Chisq)
## 1  10 -133.16                     
## 2   9 -134.28 -1 2.2502     0.1336

ls.m19 <- glmmTMB(PIRI ~ Treat_Type + avgLD_l + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
summary(ls.m19) #284.6

##  Family: poisson  ( log )
## Formula:          
## PIRI ~ Treat_Type + avgLD_l + offset(l.TFT) + (1 | Site/Plot_No)
## Data: ls.all3
## 
##      AIC      BIC   logLik deviance df.resid 
##    284.6    318.3   -134.3    268.6      490 
## 
## Random effects:
## 
## Conditional model:
##  Groups       Name        Variance Std.Dev.
##  Plot_No:Site (Intercept) 2.096    1.448   
##  Site         (Intercept) 9.911    3.148   
## Number of obs: 498, groups:  Plot_No:Site, 498; Site, 47
## 
## Conditional model:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)         -8.7216     1.8797  -4.640 3.49e-06 ***
## Treat_TypeFallRx     2.8500     1.8888   1.509  0.13133    
## Treat_TypeHarvest    5.7603     2.0508   2.809  0.00497 ** 
## Treat_TypeMowRx      2.1656     1.8477   1.172  0.24117    
## Treat_TypeSpringRx   2.7379     2.1166   1.294  0.19582    
## avgLD_l             -1.1665     0.5173  -2.255  0.02413 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

lrtest(ls.m18, ls.m19) #0.98

## Likelihood ratio test
## 
## Model 1: PIRI ~ Treat_Type + avgLD_l + l.Veg_Total + offset(l.TFT) + (1 | 
##     Site/Plot_No)
## Model 2: PIRI ~ Treat_Type + avgLD_l + offset(l.TFT) + (1 | Site/Plot_No)
##   #Df  LogLik Df Chisq Pr(>Chisq)
## 1   9 -134.28                    
## 2   8 -134.28 -1 6e-04     0.9812

ls.m20 <- glmmTMB(PIRI ~ Treat_Type + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(ls.m20) #288

## [1] 288.0281

lrtest(ls.m19, ls.m20) # p = 0.02

## Likelihood ratio test
## 
## Model 1: PIRI ~ Treat_Type + avgLD_l + offset(l.TFT) + (1 | Site/Plot_No)
## Model 2: PIRI ~ Treat_Type + offset(l.TFT) + (1 | Site/Plot_No)
##   #Df  LogLik Df  Chisq Pr(>Chisq)  
## 1   8 -134.28                       
## 2   7 -137.01 -1 5.4624    0.01943 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

rm(ls.m13, ls.m14, ls.m15, ls.m16, ls.m17, ls.m18, ls.m20)

Ok, test model fit with treat type (same as model 11)

ls.m19_sr <- simulateResiduals(ls.m19, n = 1000, plot = TRUE) #passes KS, quantile deviations fails, but it still could be an accepted model

testResiduals(ls.m19_sr) #pases

## $uniformity
## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  simulationOutput$scaledResiduals
## D = 0.055868, p-value = 0.0893
## alternative hypothesis: two-sided
## 
## 
## $dispersion
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.6209e-05, p-value = 0.556
## alternative hypothesis: two.sided
## 
## 
## $outliers
## 
##  DHARMa bootstrapped outlier test
## 
## data:  simulationOutput
## outliers at both margin(s) = 0, observations = 498, p-value = 1
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.000000000 0.009086345
## sample estimates:
## outlier frequency (expected: 0.0014859437751004 ) 
##                                                 0

## $uniformity
## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  simulationOutput$scaledResiduals
## D = 0.055868, p-value = 0.0893
## alternative hypothesis: two-sided
## 
## 
## $dispersion
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.6209e-05, p-value = 0.556
## alternative hypothesis: two.sided
## 
## 
## $outliers
## 
##  DHARMa bootstrapped outlier test
## 
## data:  simulationOutput
## outliers at both margin(s) = 0, observations = 498, p-value = 1
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.000000000 0.009086345
## sample estimates:
## outlier frequency (expected: 0.0014859437751004 ) 
##                                                 0

testZeroInflation(ls.m19_sr) #passes

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = 1.005, p-value = 0.94
## alternative hypothesis: two.sided

testQuantiles(ls.m19_sr) #fails

## 
##  Test for location of quantiles via qgam
## 
## data:  simulationOutput
## p-value = 0.00256
## alternative hypothesis: both

Trying pairwise comparison of model fit:

emmeans(ls.m19, specs = pairwise ~ Treat_Type, adjust = 'Tukey', type = 'response')

## $emmeans
##  Treat_Type     rate       SE  df asymp.LCL asymp.UCL
##  Control    0.000168 0.000292 Inf  5.63e-06   0.00503
##  FallRx     0.002912 0.004627 Inf  1.29e-04   0.06559
##  Harvest    0.053471 0.089469 Inf  2.01e-03   1.42029
##  MowRx      0.001469 0.002266 Inf  7.14e-05   0.03022
##  SpringRx   0.002603 0.004432 Inf  9.25e-05   0.07322
## 
## Confidence level used: 0.95 
## Intervals are back-transformed from the log scale 
## 
## $contrasts
##  contrast              ratio       SE  df null z.ratio p.value
##  Control / FallRx    0.05785  0.10926 Inf    1  -1.509  0.5566
##  Control / Harvest   0.00315  0.00646 Inf    1  -2.809  0.0399
##  Control / MowRx     0.11468  0.21190 Inf    1  -1.172  0.7674
##  Control / SpringRx  0.06471  0.13696 Inf    1  -1.294  0.6952
##  FallRx / Harvest    0.05446  0.10554 Inf    1  -1.502  0.5613
##  FallRx / MowRx      1.98261  3.42698 Inf    1   0.396  0.9948
##  FallRx / SpringRx   1.11861  2.24433 Inf    1   0.056  1.0000
##  Harvest / MowRx    36.40752 69.35922 Inf    1   1.887  0.3243
##  Harvest / SpringRx 20.54164 43.83726 Inf    1   1.416  0.6171
##  MowRx / SpringRx    0.56421  1.10087 Inf    1  -0.293  0.9984
## 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## Tests are performed on the log scale

Control vs Harvest is the only significant different (p = 0.0399)

I’d like to see some graphs before I close out

library(sjPlot)
library(sjlabelled)

## 
## Attaching package: 'sjlabelled'

## The following object is masked from 'package:forcats':
## 
##     as_factor

## The following object is masked from 'package:ggplot2':
## 
##     as_label

## The following object is masked from 'package:dplyr':
## 
##     as_label

library(sjmisc)

## 
## Attaching package: 'sjmisc'

## The following object is masked from 'package:purrr':
## 
##     is_empty

## The following object is masked from 'package:tidyr':
## 
##     replace_na

## The following object is masked from 'package:tibble':
## 
##     add_case

set_theme(base = theme_classic(),
          theme.font = 'serif',
          axis.title.size = 1.5,
          axis.textsize.x = 1.5,
          axis.textsize.y = 1.5,
          title.size = 2.5,
          title.align = "center",
          legend.pos = "right",
          legend.size = 1.5,
          legend.title.size = 1.5,
          #legend.bordercol = "black",
          legend.item.size = .75)

# plot_model(ls.m19) #this is incidence rate ratios
# 
# plot_model(ls.m19, type = "diag") #random vs normal quantiles
# 
# plot_model(ls.m19, type = "re") #this plots random effects
# 
# plot_model(ls.m19, 
#            type = 'pred', 
#            terms = 'Treat_Type') #plot marginal effects (i might have done this wrong)

# LS PIRI plot 1 ------------------- LD vs. TT
plot_model(ls.m19, 
                       type = 'pred', 
                       terms = c('avgLD_l', 'Treat_Type'),
                       axis.title = c("Average Leaf Litter Depth (log transformed)", "Total Count of Pitch Pine"),
                       title = "Predicted Counts of Pitch Pine Seedlings \n >/=50cm & <2.5cm DBH",
           legend.title = "Treatment Type",
           line.size = 1,
           value.offset = 'Treat_Type',
           ci.lvl = NA,
           colors = c("#D8B70A", "#02401B", "#A2A475", "#81A88D", "#972D15"))

## Warning in checkTerms(data.tmb1$terms, data.tmb0$terms): Predicting new random effect levels for terms: 1 | Plot_No:Site
## Disable this warning with 'allow.new.levels=TRUE'

# LS PIRI plot 2 ------------------- Time from Treatment vs TT
plot_model(ls.m19, 
                       type = 'pred', 
                       terms = c('l.TFT', 'Treat_Type'),
                       axis.title = c("Time from Treatment (log transformed)", "Total Count of Pitch Pine"),
                       title = "Predicted Counts of Pitch Pine Seedlings \n >/=50cm & <2.5cm DBH",
           legend.title = "Treatment Type",
           line.size = 1,
           value.offset = 'Treat_Type',
           ci.lvl = NA,
           colors = c("#D8B70A", "#02401B", "#A2A475", "#81A88D", "#972D15"))

## Warning in checkTerms(data.tmb1$terms, data.tmb0$terms): Predicting new random effect levels for terms: 1 | Plot_No:Site
## Disable this warning with 'allow.new.levels=TRUE'

Should I think about graphs where the offset is included?

Random slope for models?

Shrub oak models

so.ls1 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.BA_piri + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(so.ls1) # 3377.9

# test piri ba
so.ls2 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(so.ls2) #3379.8

lrtest(so.ls1, so.ls2) #p = 0.0498, so maybe

# test total BA
so.ls3 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_piri + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(so.ls3) #3379.8

lrtest(so.ls1, so.ls3) # p = 0.0478, keep BA for now

# test mineral
so.ls4 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(so.ls4) #3381.7

lrtest(so.ls2, so.ls4) # p = 0.048

# test avg ld
so.ls5 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(so.ls5) #3378.3 ---- keep mineral, lower AIC

lrtest(so.ls5, so.ls2) # p = 0.48

# test piri
so.ls6 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.BA_HA + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(so.ls6) #3380

lrtest(so.ls6, so.ls5) # p = 0.051

# test other
so.ls7 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.BA_HA + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(so.ls7) #3388.2

lrtest(so.ls6, so.ls7) # p = 0.001

# test veg
so.ls8 <- glmmTMB(Shrub_Oak ~ Treat_Type +l.other + l.BA_HA + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(so.ls8) # 3383.4




lrtest(so.ls8, so.ls6) # p > 0.001

Seemingly best models

so.ls5 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(so.ls5) #3378.3 ---- keep mineral, lower AIC

so.ls5_sr <- simulateResiduals(so.ls5, n = 1000, plot = TRUE) #quantile test not looking good
testResiduals(so.ls5_sr)
testDispersion(so.ls5_sr, alternative = "less") #again, under dispersed

so.ls5a <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = compois)
AIC(so.ls5a) #3290

so.ls5a_sr <- simulateResiduals(so.ls5a, n = 1000, plot = TRUE) 
testResiduals(so.ls5a_sr) #still underdispersed

so.ls5b <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = genpois)
AIC(so.ls5b) #3274.5

so.ls5b_sr <- simulateResiduals(so.ls5b, n = 1000, plot = TRUE)
testResiduals(so.ls5b_sr) #still underdispersed

Test second model

so.ls6 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.BA_HA + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(so.ls6) #3380

so.ls6_sr <- simulateResiduals(so.ls6, n = 1000, plot = TRUE)
testResiduals(so.ls6_sr) # fails dispersion

so.ls6a <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.BA_HA + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = genpois)
AIC(so.ls6a) #3275.5
#compois distro fails and won't produce DHARMa results
so.ls6a_sr <- simulateResiduals(so.ls6a, n = 1000, plot = T)
testResiduals(so.ls6a_sr) # fails dispersion

testing other distros

so.ls6 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.BA_HA + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(so.ls6)

so.ls6a <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.BA_HA + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = genpois)
AIC(so.ls6a)

# compois distro fails, won't produce DHARMa results

so.ls6b <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.BA_HA + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = tweedie)
AIC(so.ls6b)

so.ls6b_sr <- simulateResiduals(so.ls6b, n = 1000, plot = TRUE)
testDispersion(so.ls6b_sr) #fails

so.ls6c <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.BA_HA + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = t_family)
AIC(so.ls6c)

so.ls6c_sr <- simulateResiduals(so.ls6c, n = 1000, plot = TRUE)
testResiduals(so.ls6c_sr)
testDispersion(so.ls6c_sr) #passes
testQuantiles(so.ls6c_sr)


so.ls6d <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.BA_HA + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = gaussian)
AIC(so.ls6d)

so.ls6d_sr <- simulateResiduals(so.ls6d, n = 1000, plot = TRUE) #fails
plot(so.ls6d_sr)
testResiduals(so.ls6d_sr)

student t distro

so.ls1 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.BA_piri + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = t_family)
AIC(so.ls1) # 3880.6

# test piri ba
so.ls2 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                family = t_family)
AIC(so.ls2) #3879.6

lrtest(so.ls1, so.ls2) #p = 0.3

# test total BA
so.ls3 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_piri + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                  family = t_family)
AIC(so.ls3) #3885.4

lrtest(so.ls1, so.ls3) # p = 0.009

# test mineral
so.ls4 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                  family = t_family)
AIC(so.ls4) #3881.5

lrtest(so.ls2, so.ls4) # p = 0.046

# test avg ld
so.ls5 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                  family = t_family)
AIC(so.ls5) #3877.7---- keep avg LD, lower AIC

lrtest(so.ls5, so.ls2) # p = 0.48

# test piri
so.ls6 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.BA_HA + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                  family = t_family)
AIC(so.ls6) #3882.6

lrtest(so.ls6, so.ls4) # p = 0.08

# test other
so.ls7 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.BA_HA + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = t_family)
AIC(so.ls7) #3891

lrtest(so.ls6, so.ls7) # p = 0.001

# test veg
so.ls8 <- glmmTMB(Shrub_Oak ~ Treat_Type +l.other + l.BA_HA + avgLD_l + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = t_family)
#won't converge

lrtest(so.ls6, so.ls1) #p = 0.046

#issue is that I'm representing 3 measurement scales. reduce BA?

testing t distro

so.ls6 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.BA_HA + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                  family = t_family)

ls.all3$BA_10m <- (ls.all3$BA_HA/1000)
ls.all3$l.BA_10m <- log(ls.all3$BA_10m + 1)

so.ls9 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.BA_10m + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                  data = ls.all3,
                  family = t_family)

so.ls10 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                  data = ls.all3,
                  family = t_family)
AIC(so.ls10) #3884.5

so.ls11 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + avgLD_l + offset(l.TFT) + (1|Site/Plot_No),
                  data = ls.all3,
                  family = t_family)
AIC(so.ls11) #3395.4

lrtest(so.ls10, so.ls11) #keep veg

so.ls12 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + avgLD_l + l.BA_10m + offset(l.TFT) + (1|Site/Plot_No),
                  data = ls.all3,
                  family = t_family)
AIC(so.ls12) #3883.8

lrtest(so.ls11, so.ls12) #keep ba

#maybe if i drop something else model will run?

so.ls13 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.BA_10m + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                  data = ls.all3,
                  family = t_family)
AIC(so.ls12) #3883.8

so.ls1a <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_10m + l.BA_piri + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = t_family)
AIC(so.ls1a) # 3873.8

starting with basal area at 10m² range instead of per HA

# this is killing me - moving the basal area down to the 10m scale, to keep measurements on 2 scales; overwriting models

so.ls1 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_10m + l.BA_piri + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = t_family)
AIC(so.ls1) # 3873.8

so.ls2 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_10m + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = t_family)
AIC(so.ls2)

so.ls3 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_10m + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = t_family)
AIC(so.ls3) #3875.7 without mineral

so.ls3b <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_10m + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = t_family)
AIC(so.ls3b) #3871.9 w/o LD

lrtest(so.ls2, so.ls3) # p = 0.46
lrtest(so.ls2, so.ls3b) #drop ld for sure

# test piri
so.ls4 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.BA_10m + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = t_family)
AIC(so.ls4) #3871.9

#test other
so.ls5 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.BA_10m + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = t_family)
AIC(so.ls5) #3881.5

lrtest(so.ls4, so.ls5) # keep other

# test veg
so.ls6 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.BA_10m + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = t_family)
AIC(so.ls6) #3878

so.ls7 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = t_family)
AIC(so.ls7) #3881.7, keep ba

best model?

so.ls6 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.BA_10m + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = t_family)
summary(so.ls6)

so.ls6_sr <- simulateResiduals(so.ls6, n = 1000, plot = TRUE)
testResiduals(so.ls6_sr)
plot(so.ls6_sr)

Pairwise with treatment type

emmeans(so.ls6, specs = pairwise ~ Treat_Type, adjust = 'Tukey', type = 'response')

hist(ls.all3$Shrub_Oak)

ggplot(ls.all3, aes(x=Shrub_Oak))+
  geom_histogram(binwidth = 2)+
  facet_grid(rows = vars(Treat_Type))

Model 6 appears to be the best. There are issues of collinearlity with total veg cover and shrub oak counts, so it has been dropped from the model despite AIC/lrtest results

Graph shrub oak models

# LS SO plot 1 ------------------- other vs. TT
plot_model(so.ls6, 
                       type = 'pred', 
                       terms = c('l.other', 'Treat_Type'),
                       axis.title = c("Other small seedling counts (log transformed)", "Total Count of Shrub Oak"),
                       title = "Predicted Counts of Shrub Oak \n Seedlings >/=50cm & <2.5cm DBH",
           legend.title = "Treatment Type",
           line.size = 1,
           value.offset = 'Treat_Type',
           ci.lvl = NA,
           colors = c("#D8B70A", "#02401B", "#A2A475", "#81A88D", "#972D15")) 

# LS SO plot 2 ------------------- BA_10m vs. TT
plot_model(so.ls6, 
                       type = 'pred', 
                       terms = c('l.other', 'Treat_Type'),
                       axis.title = c("Total Basal Area per 10 square meters (log transformed)", "Total Count of Shrub Oak"),
                       title = "Predicted Counts of Shrub Oak \n Seedlings >/=50cm & <2.5cm DBH",
           legend.title = "Treatment Type",
           line.size = 1,
           value.offset = 'Treat_Type',
           ci.lvl = NA,
           colors = c("#D8B70A", "#02401B", "#A2A475", "#81A88D", "#972D15")) 

# LS SO plot 3 ------------------- l.mineral vs. TT
plot_model(so.ls6, 
                       type = 'pred', 
                       terms = c('l.other', 'Treat_Type'),
                       axis.title = c("Amount of exposed mineral soil (log transformed)", "Total Count of Shrub Oak"),
                       title = "Predicted Counts of Shrub Oak \n Seedlings >/=50cm & <2.5cm DBH",
           legend.title = "Treatment Type",
           line.size = 1,
           value.offset = 'Treat_Type',
           ci.lvl = NA,
           colors = c("#D8B70A", "#02401B", "#A2A475", "#81A88D", "#972D15"))

#Start model fit with genpois distro and then do elimination?

so.ls1 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.BA_piri + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = genpois)
AIC(so.ls1) #3275.5


so.ls1b <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.BA_piri + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = compois)
AIC(so.ls1b) #this is taking a very long time - so i quit, moving forward with genpois

#test piri ba
so.ls2 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = genpois)
AIC(so.ls2) #3276.4

lrtest(so.ls1, so.ls2) #drop

#test ba
so.ls3 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = genpois)
AIC(so.ls3) #3276.8

#test mineral
so.ls4 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = genpois)
AIC(so.ls4) #3279.2

lrtest(so.ls3, so.ls4) #keep mineral

# test ld
so.ls5 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = genpois)
AIC(so.ls5) #3274.8

# test other
so.ls6 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI  + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = genpois)
AIC(so.ls6) #3280

lrtest(so.ls6, so.ls5) #keep other

# test piri
so.ls7 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.Mineral + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = genpois)
AIC(so.ls7) #3275.9

lrtest(so.ls7, so.ls5) #drop piri

#going to drop veg due to collinearity issues
# test ld
so.ls8 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = genpois)
AIC(so.ls8) #3284

so.ls8_sr <- simulateResiduals(so.ls8, n = 1000, plot = TRUE)
testResiduals(so.ls8_sr)
testDispersion(so.ls8_sr, alternative = "less")
#underdispersed
testZeroInflation(so.ls8_sr)

so.ls9 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = compois)
AIC(so.ls9) #3301.9

so.ls9_sr <- simulateResiduals(so.ls9, n = 1000, plot = TRUE)
testResiduals(so.ls9_sr) #worse on dispersion

so.ls10 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = nbinom1)
AIC(so.ls10) #3257.3

so.ls10_sr <- simulateResiduals(so.ls10, n = 1000, plot = TRUE)
testResiduals(so.ls10_sr) #fails dispersion


so.ls11 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = nbinom2)
AIC(so.ls11) #3352

so.ls11_sr <- simulateResiduals(so.ls11, n = 1000, plot = TRUE)
testResiduals(so.ls11_sr) # fails dispersion
testZeroInflation(so.ls11_sr)

# run poisson model with zero inflation?
so.ls12 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 ziformula = ~1,
                 family = genpois)
AIC(so.ls12) #3271.5

so.ls12_sr <- simulateResiduals(so.ls12, n = 1000, plot = TRUE)
testResiduals(so.ls12_sr) #still fails dispersion
testZeroInflation(so.ls12_sr)

so.ls13 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(so.ls13)

so.ls13_sr <- simulateResiduals(so.ls13, n = 1000, plot = TRUE)
testResiduals(so.ls13_sr) #still fails dispersion
testZeroInflation(so.ls13_sr) #does fail

ok, zero inflation issues

#starting with veg dropped due to collinearity issues
so.ls1 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.BA_piri + l.Mineral + avgLD_l + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 ziformula = ~1,
                 family = nbinom1)
AIC(so.ls1) #3253.5

#test piri ba
so.ls2 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + avgLD_l + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 ziformula = ~1,
                 family = nbinom1)
AIC(so.ls2) #3252

#test ba
so.ls3 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.Mineral + avgLD_l + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 ziformula = ~1,
                 family = nbinom1)
AIC(so.ls3) #3254

lrtest(so.ls2, so.ls3) # p = 0.6, drop

#test ld
so.ls4 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 ziformula = ~1,
                 family = nbinom1)
AIC(so.ls4) #3252

#test piri
so.ls5 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 ziformula = ~1,
                 family = nbinom1)
AIC(so.ls5) #3254

lrtest(so.ls4, so.ls5) # keep piri

#test other
so.ls6 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI  + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 ziformula = ~1,
                 family = nbinom1)
AIC(so.ls6) # 3256

lrtest(so.ls4, so.ls6) #keep other

#test mineral
so.ls7 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 ziformula = ~1,
                 family = nbinom1)
AIC(so.ls7) #3255.4

lrtest(so.ls7, so.ls4) #keep mineral

#test treat type
so.ls8 <- glmmTMB(Shrub_Oak ~ l.PIRI + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 ziformula = ~1,
                 family = nbinom1)
AIC(so.ls8) #3341.4

lrtest(so.ls4, so.ls8) # keep tt

seemingly best model

so.ls4 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 ziformula = ~1,
                 family = nbinom1)

so.ls4_sr <- simulateResiduals(so.ls4, n = 1000, plot = TRUE)
testZeroInflation(so.ls4_sr) #passes
testResiduals(so.ls4_sr) #fails dispersion

so.ls4b <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 ziformula = ~Treat_Type,
                 family = nbinom1)

so.ls4b_sr <- simulateResiduals(so.ls4b, n = 1000, plot = TRUE)
testZeroInflation(so.ls4b_sr) #passes
testResiduals(so.ls4b_sr)

#try with second nbinom distro

so.ls4c <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 ziformula = ~1,
                 family = nbinom2)

so.ls4c_sr <- simulateResiduals(so.ls4c, n = 1000, plot = TRUE)
testZeroInflation(so.ls4c_sr) #passes
testResiduals(so.ls4c_sr) #worse

so.ls4d <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 ziformula = ~Treat_Type,
                 family = nbinom2)

so.ls4d_sr <- simulateResiduals(so.ls4d, n = 1000, plot = TRUE)
testZeroInflation(so.ls4d_sr) #passes
testResiduals(so.ls4d_sr) #worse

# maybe 
so.ls4e <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 ziformula = ~Treat_Type,
                 family = genpois)

so.ls4e_sr <- simulateResiduals(so.ls4e, n = 1000, plot = TRUE)
testZeroInflation(so.ls4e_sr) #passes
testResiduals(so.ls4e_sr) #fails again

starting modeling again. going to model with poisson and check out zero inflation issues. then reduce model or try other distros

m1 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = poisson)
AIC(m1) #3391.3

m1_sr <- simulateResiduals(m1, n = 1000, plot = T)
testZeroInflation(m1_sr)#fails

m2 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
              ziformula = ~1,
                 family = poisson)
#1, region, and tt all fail with poisson distro

m2 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
              data = ls.all3,
              family = nbinom1)
AIC(m2) #3254.1

m2_sr <- simulateResiduals(m2, n = 1000, plot = T)
testZeroInflation(m2_sr) #passes
testResiduals(m2_sr) # fails, is underdispersed
testDispersion(m2_sr, alternative = "less") # = 0.006

#try nbimom2
m3 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
              data = ls.all3,
              family = nbinom2)
AIC(m3) #3313.1

m3_sr <- simulateResiduals(m3, n = 1000, plot = T)
testZeroInflation(m3_sr) #fails
testResiduals(m3_sr) # fails, is underdispersed
testDispersion(m3_sr, alternative = "less") # <0.001

#try genpois (it fails)
#try compois - taking a very long time to run
m4 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
              data = ls.all3,
              ziformula = ~1,
              family = compois)
AIC(m4) #3270

m4_sr <- simulateResiduals(m4, n = 1000, plot = T)
testZeroInflation(m4_sr) #passes
testResiduals(m4_sr) # fails, is underdispersed
testDispersion(m4_sr, alternative = "less") # <0.001

#compois is worse

m5 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
              data = ls.all3,
              family = nbinom1)

m5_sr <- simulateResiduals(m5, n = 1000, plot = T)
testZeroInflation(m5_sr) #passes
testResiduals(m5_sr) #0.006

#region is slightly worse (0.002) for ziformula; treat_type is 0.006; site fails, l.TFT is 0.006;BAHA fails 

# maybe having less variables will change the sr model

m6 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
              data = ls.all3,
              family = nbinom1)

m6_sr <- simulateResiduals(m6, n = 1000, plot = T)
testZeroInflation(m6_sr) #passes
testResiduals(m6_sr) #worse 0.002

m7 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.other + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
              data = ls.all3,
              ziformula = ~1,
              family = nbinom1)

m7_sr <- simulateResiduals(m7, n = 1000, plot = T)
testZeroInflation(m7_sr) #passes
testResiduals(m7_sr) #0.008


m8 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
              data = ls.all3,
              ziformula = ~1,
              family = nbinom1)

m8_sr <- simulateResiduals(m8, n = 1000, plot = T)
testZeroInflation(m8_sr) #passes
testResiduals(m8_sr) #0.002

m10 <- glmmTMB(Shrub_Oak ~ Treat_Type + offset(l.TFT) + (1|Site/Plot_No),
              data = ls.all3,
              ziformula = ~1,
              family = nbinom1)

m10_sr <- simulateResiduals(m10, n = 1000, plot = T)
testZeroInflation(m10_sr) #passes
testResiduals(m10_sr) #0.004

m11 <- glmmTMB(Shrub_Oak ~ Treat_Type + offset(l.TFT) + (1|Site/Plot_No),
              data = ls.all3,
              family = nbinom1)

m11_sr <- simulateResiduals(m11, n = 1000, plot = T)
testZeroInflation(m11_sr) #passes
testResiduals(m11_sr) #0.004


m11 <- glmmTMB(Shrub_Oak ~ Treat_Type + offset(l.TFT) + (1|Site/Plot_No),
              data = ls.all3,
              ziformula = ~1,
              family = gaussian)

m11_sr <- simulateResiduals(m11, n = 1000, plot = T)
testZeroInflation(m11_sr) #passes
testResiduals(m11_sr) #0.004


m2 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
              ziformula = ~1,
                 family = gaussian)
AIC(m2)
m2_sr <- simulateResiduals(m2, n = 1000, plot = T)
testZeroInflation(m2)
testResiduals(m2) #fails uniformity, passes dispersion

m2 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
              data = ls.all3,
              ziformula = ~1,
              family = tweedie)
AIC(m2)
m2_sr <- simulateResiduals(m2, n = 1000, plot = T)
testZeroInflation(m2)
testResiduals(m2) #fails dispersion passes uniformity

m2 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.Mineral + offset(l.TFT) + (1|Site/Plot_No),
              data = ls.all3,
              family = tweedie)
AIC(m2)
m2_sr <- simulateResiduals(m2, n = 1000, plot = T)
testZeroInflation(m2)
testResiduals(m2) #fails dispersion passes uniformity

nbinom distro 1 solves 0 inflation issues, but models are still underdispersed

I’ve tried poisson, nbinom1, nbinom1 with ZI, nbinom2, genpois, compois, tweedie, gaussian and none of these distros work. They are all underdispersed

These are very full models with many different distributions. All are underdispersed.

m1 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.BA_piri + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = nbinom1)
AIC(m1)

## [1] 3250.579

m1_sr <- simulateResiduals(m1, n = 1000, plot = T)

testZeroInflation(m1_sr) #passes

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = 1.0111, p-value = 0.958
## alternative hypothesis: two.sided

testResiduals(m1_sr) #under dispersed, 0.002

## $uniformity
## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  simulationOutput$scaledResiduals
## D = 0.030412, p-value = 0.7464
## alternative hypothesis: two-sided
## 
## 
## $dispersion
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 0.18385, p-value = 0.002
## alternative hypothesis: two.sided
## 
## 
## $outliers
## 
##  DHARMa bootstrapped outlier test
## 
## data:  simulationOutput
## outliers at both margin(s) = 0, observations = 498, p-value = 1
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.00000000 0.01004016
## sample estimates:
## outlier frequency (expected: 0.00142570281124498 ) 
##                                                  0

## $uniformity
## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  simulationOutput$scaledResiduals
## D = 0.030412, p-value = 0.7464
## alternative hypothesis: two-sided
## 
## 
## $dispersion
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 0.18385, p-value = 0.002
## alternative hypothesis: two.sided
## 
## 
## $outliers
## 
##  DHARMa bootstrapped outlier test
## 
## data:  simulationOutput
## outliers at both margin(s) = 0, observations = 498, p-value = 1
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.00000000 0.01004016
## sample estimates:
## outlier frequency (expected: 0.00142570281124498 ) 
##                                                  0

m2 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.BA_piri + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = genpois)
AIC(m2)

## [1] 3275.496

m2_sr <- simulateResiduals(m2, n = 1000, plot = T)

testZeroInflation(m2_sr) #passes

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = 1.1026, p-value = 0.49
## alternative hypothesis: two.sided

testResiduals(m2_sr) #under dispersed, 0.004

## $uniformity
## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  simulationOutput$scaledResiduals
## D = 0.040086, p-value = 0.4003
## alternative hypothesis: two-sided
## 
## 
## $dispersion
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 0.12407, p-value = 0.004
## alternative hypothesis: two.sided
## 
## 
## $outliers
## 
##  DHARMa bootstrapped outlier test
## 
## data:  simulationOutput
## outliers at both margin(s) = 0, observations = 498, p-value = 1
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.00000000 0.01004016
## sample estimates:
## outlier frequency (expected: 0.00170682730923695 ) 
##                                                  0

## $uniformity
## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  simulationOutput$scaledResiduals
## D = 0.040086, p-value = 0.4003
## alternative hypothesis: two-sided
## 
## 
## $dispersion
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 0.12407, p-value = 0.004
## alternative hypothesis: two.sided
## 
## 
## $outliers
## 
##  DHARMa bootstrapped outlier test
## 
## data:  simulationOutput
## outliers at both margin(s) = 0, observations = 498, p-value = 1
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.00000000 0.01004016
## sample estimates:
## outlier frequency (expected: 0.00170682730923695 ) 
##                                                  0

m3 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.BA_piri + l.Mineral + avgLD_l + l.Veg_Total + offset(l.TFT) + (1|Site/Plot_No),
                 data = ls.all3,
                 family = compois)
AIC(m3) #this is taking a very long time to run

## [1] 3291.184

m3_sr <- simulateResiduals(m3, n = 1000, plot = T)

testZeroInflation(m3_sr) #fails

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = 1.3529, p-value = 0.028
## alternative hypothesis: two.sided

testResiduals(m3_sr) #fails dispersion

## $uniformity
## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  simulationOutput$scaledResiduals
## D = 0.037942, p-value = 0.4703
## alternative hypothesis: two-sided
## 
## 
## $dispersion
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 0.042059, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 
## 
## $outliers
## 
##  DHARMa bootstrapped outlier test
## 
## data:  simulationOutput
## outliers at both margin(s) = 0, observations = 498, p-value = 1
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.00000000 0.01511044
## sample estimates:
## outlier frequency (expected: 0.001285140562249 ) 
##                                                0

## $uniformity
## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  simulationOutput$scaledResiduals
## D = 0.037942, p-value = 0.4703
## alternative hypothesis: two-sided
## 
## 
## $dispersion
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 0.042059, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 
## 
## $outliers
## 
##  DHARMa bootstrapped outlier test
## 
## data:  simulationOutput
## outliers at both margin(s) = 0, observations = 498, p-value = 1
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.00000000 0.01511044
## sample estimates:
## outlier frequency (expected: 0.001285140562249 ) 
##                                                0

m4 <- glmmTMB(Shrub_Oak ~ Treat_Type + l.PIRI + l.other + l.BA_HA + l.Mineral +  offset(l.TFT) + (1|Site/Plot_No),
              data = ls.all3,
              ziformula = ~1,
              family = compois)
AIC(m4)

## [1] 3269.97

m4_sr <- simulateResiduals(m4, n = 1000, plot = T)

testZeroInflation(m4_sr) #passes

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = 1.097, p-value = 0.462
## alternative hypothesis: two.sided

testResiduals(m4_sr) #fails dispersion

## $uniformity
## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  simulationOutput$scaledResiduals
## D = 0.028524, p-value = 0.8125
## alternative hypothesis: two-sided
## 
## 
## $dispersion
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 0.051874, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 
## 
## $outliers
## 
##  DHARMa bootstrapped outlier test
## 
## data:  simulationOutput
## outliers at both margin(s) = 0, observations = 498, p-value = 1
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.00000000 0.01531124
## sample estimates:
## outlier frequency (expected: 0.00116465863453815 ) 
##                                                  0

## $uniformity
## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  simulationOutput$scaledResiduals
## D = 0.028524, p-value = 0.8125
## alternative hypothesis: two-sided
## 
## 
## $dispersion
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 0.051874, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 
## 
## $outliers
## 
##  DHARMa bootstrapped outlier test
## 
## data:  simulationOutput
## outliers at both margin(s) = 0, observations = 498, p-value = 1
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.00000000 0.01531124
## sample estimates:
## outlier frequency (expected: 0.00116465863453815 ) 
##                                                  0

Large Seedling Linear Regression

Kathleen Stutzman

2024-01-10

The above script a data naming conventions are the same as in the LS_GLMM script

Shrub oak models

testing other distros

student t distro

testing t distro

starting with basal area at 10m² range instead of per HA

best model?

Pairwise with treatment type

Model 6 appears to be the best. There are issues of collinearlity with total veg cover and shrub oak counts, so it has been dropped from the model despite AIC/lrtest results

Graph shrub oak models

ok, zero inflation issues

starting modeling again. going to model with poisson and check out zero inflation issues. then reduce model or try other distros

nbinom distro 1 solves 0 inflation issues, but models are still underdispersed

These are very full models with many different distributions. All are underdispersed.

I cannot find a model that works.

Large Seedling Linear Regression

Kathleen Stutzman

2024-01-10

The above script a data naming conventions are the same as in the LS_GLMM script

Shrub oak models

testing other distros

student t distro

testing t distro

starting with basal area at 10m2 range instead of per HA

best model?

Pairwise with treatment type

Model 6 appears to be the best. There are issues of collinearlity with total veg cover and shrub oak counts, so it has been dropped from the model despite AIC/lrtest results

Graph shrub oak models

ok, zero inflation issues

starting modeling again. going to model with poisson and check out zero inflation issues. then reduce model or try other distros

nbinom distro 1 solves 0 inflation issues, but models are still underdispersed

These are very full models with many different distributions. All are underdispersed.

I cannot find a model that works.

starting with basal area at 10m² range instead of per HA